Mission Sequence Model and Deep Reinforcement Learning-Based Replanning Method for Multi-Satellite Observation by Peiyan Li, Peixing Cui, Huiquan Wang

“…Both phases are formulated as Markov Decision Processes (MDPs) and optimized using the PPO algorithm. Extensive simulations demonstrate that our method significantly outperforms state-of-the-art approaches, achieving a 15.27% higher request insertion revenue rate and a 3.05% improvement in overall mission revenue rate, while maintaining a 1.17% lower modification rate and achieving faster computational speeds. …”
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Comparison between Logistic Regression and K-Nearest Neighbour Techniques with Application on Thalassemia Patients in Mosul by Mohammed Al jbory, Hutheyfa Taha

“… The experimental results showed that the logistic regression model performed better than the nearest neighbor algorithm with a precision of 96%, recall of 98%, and F1- score of 97% in the thalassemia intermedia category, while it had a precision of 97%, recall of 95%, and F1- score of 96% in the thalassemia major category, indicating that logistic regression performed well in distinguishing between these two categories. it has been shown that logistic regression is more effective than the K-nearest neighbor algorithm in classifying thalassemia patients, especially those with thalassemia major. …”
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Assessing tafenoquine implementation in Brazil: a qualitative evaluation of perceptions of healthcare providers and Plasmodium vivax patients (QualiTRuST Study) by Alicia P. C. Santos, Marcelo A. M. Brito, Ana P. S. Oliveira, Rafaela N. Dávila, Hiran S. S. Gama, Evellyn A. R. T. Silva, Hélio A. Amazonas Jr., Patrícia C. S. Balieiro, Rosilene Rufatto, Penny Grewal Daumerie, Cássio R. L. Peterka, Dhélio Batista Pereira, Marcus V. G. Lacerda, Felipe L. G. Murta

“…The findings underscore the importance of adaptive training to optimize P. vivax radical cure implementation.…”
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Inflammation Biomarker-Driven Vertical Visualization Model for Predicting Long-Term Prognosis in Unstable Angina Pectoris Patients with Angiographically Intermediate Coronary Lesio... by Zhou B, Tan W, Duan S, Wang Y, Bian F, Zhao P, Wang J, Yao Z, Li H, Hu X, Wang J, Liu J

“…Various inflammatory markers are employed in clinical practice to predict patient prognosis and optimize clinical decision-making in the management of ACS. …”
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Aircraft Multi-stage Altitude Prediction Under Satellite Signal Loss by Mengchan HUANG, Qiang MIAO

“…Compared to RNNs and their variants, the LTCA–TCN algorithm yields superior prediction results while maintaining a simpler structure and requiring fewer computational resources. …”
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Cell Outage Detection and Compensation in Two-Tier Heterogeneous Networks by Wenqian Xue, Hengzhi Zhang, Yong Li, Dong Liang, Mugen Peng

“…Power adjustment on each resource block is carried out via Lagrange optimizing algorithm to compensate the breakdown cell. …”
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The benefits and trade-offs of multi-variable calibration of the WaterGAP global hydrological model (WGHM) in the Ganges and Brahmaputra basins by H. M. M. Hasan, P. Döll, P. Döll, S.-M. Hosseini-Moghari, F. Papa, A. Güntner, A. Güntner

“…Pareto-dominance-based multi-objective calibration, often referred to as Pareto-optimal calibration (POC), may serve to estimate model parameter sets and analyse trade-offs among different objectives during calibration. …”
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Inversion Model for Total Nitrogen in Rhizosphere Soil of Silage Corn Based on UAV Multispectral Imagery by Hongyan Yang, Jixuan Yan, Guang Li, Weiwei Ma, Xiangdong Yao, Jie Li, Qihong Da, Xuchun Li, Kejing Cheng

“…The optimal period for TN inversion was determined. The SVM-RFE algorithm outperformed the models built without feature selection in terms of accuracy. …”
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Establishing strength prediction models for low-carbon rubberized cementitious mortar using advanced AI tools by Fu Limei, Xu Feng

“…However, their reduced compressive strength (CS) due to the inclusion of rubber remains a significant barrier to widespread adoption. While several experimental studies exist, there is a clear gap in utilizing data-driven strategies to efficiently predict and optimize the strength performance of such materials. …”
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Know your competitor! Analyzing and predicting the location of competing stores: The case study of Valora at Swiss railway stations by Thomas Wieland

“…Binary logit models are built for the formats examined in order to identify the determinants of location choice. Machine learning algorithms are used to check and optimize the predictive ability of the models. …”
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What factors enhance students' achievement? A machine learning and interpretable methods approach. by Hui Mao, Ribesh Khanal, ChengZhang Qu, HuaFeng Kong, TingYao Jiang

“…The results advocate for: (a) teachers should balance direct instruction with active learning modalities to optimize achievement, and (b) early warning systems should leverage identifiable learning features to proactively support struggling students. …”
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Training a Minesweeper Agent Using a Convolutional Neural Network by Wenbo Wang, Chengyou Lei

“…The results showed that in a 6 × 6 grid with four mines, the DQN model achieved an average win rate of 93.3% (standard deviation: 0.77%), while the supervised learning method achieved 91.2% (standard deviation: 0.9%), both outperforming human players and baseline algorithms and demonstrating high intelligence. …”
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Evaluation of Liver Fibrosis through Noninvasive Tests in Steatotic Liver Disease by Yuri Cho

“…The integration of multiple non-invasive methods can optimize diagnostic accuracy, reducing the need for invasive biopsies while identifying patients at risk of liver-related complications. …”
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Leveraging machine learning to evaluate the effect of raw materials on the compressive strength of ultra-high-performance concrete by Mohamed Abdellatief, G. Murali, Saurav Dixit

“…The impact of 12 influential features on CS was evaluated to optimize the performance of the proposed models. Among the algorithms, XGB outperformed the others with an R² of 90.1 % and a lower RMSE of 11.52 MPa, surpassing RF (88.7 %), GB (89.2 %), and GPR (86.1 %). …”
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Efficient Resources Provisioning Based on Load Forecasting in Cloud by Rongdong Hu, Jingfei Jiang, Guangming Liu, Lixin Wang

“…Cloud providers should ensure QoS while maximizing resources utilization. One optimal strategy is to timely allocate resources in a fine-grained mode according to application’s actual resources demand. …”
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Improved Variational Mode Decomposition in Pipeline Leakage Detection at the Oil Gas Chemical Terminals Based on Distributed Optical Fiber Acoustic Sensing System by Hongxuan Xu, Jiancun Zuo, Teng Wang

“…This paper employs a distributed fiber optic sensing system to collect pipeline leakage signals and processes these signals using the traditional variational mode decomposition (VMD) algorithm. While traditional VMD methods require manual parameter setting, which can lead to suboptimal decomposition results if parameters are incorrectly chosen, our proposed method introduces an improved particle swarm optimization algorithm to automatically determine the optimal parameters. …”
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The interpretable machine learning model for depression associated with heavy metals via EMR mining method by Site Xu, Mu Sun

“…We constructed 5 ML models to detect depression based on heavy metal exposure and assessed their performance using 10 discrimination metrics. The optimal model was selected after parameter tuning with a Genetic Algorithm (GA). …”
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Subspace-based local compilation of variational quantum circuits for large-scale quantum many-body simulation by Shota Kanasugi, Yuichiro Hidaka, Yuya O. Nakagawa, Shoichiro Tsutsui, Norifumi Matsumoto, Kazunori Maruyama, Hirotaka Oshima, Shintaro Sato

“…The LSVQC performs a variational optimization to reproduce the action of the target time-evolution operator within a physically reasonable subspace. …”
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DECISION SUPPORT SYSTEM IN PLANNING INVESTMENT PROJECTS by Viktor Kosenko

“…The following results were obtained: it is shown that the efficiency of the investment process is related to the assessment and selection of the most attractive investment projects from a number of alternatives, which will provide the maximum profit in the future; the strategic tasks of investing in the development of organizations and enterprises are singled out; the main finances that can be involved in carrying out an investment project are considered; the concepts of an investment project, a subject and an object of the investment activity were defined; the above concepts were formalized using the theory of sets and the system approach; the basic conditions under which the project should be implemented are formulated; the generalized diagram of the investment project implementation is developed. While developing the method of selecting the optimal variant of the investment project, the types of source information about the project and the organization which will use the finances are involved are determined. …”
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Research into prediction and influential factors of circuit breaker closing time using BFGS-NN by Longcheng Dai, Jiaying Yu, Zhihui Huang, Hui Ni, Yifan Zhang, Junting Dou

“…On-site operational data were analyzed to build a circuit breaker action time database. The BFGS algorithm trained on these data generated a closing time prediction model, achieving rapid convergence and optimal fit during learning. …”
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